Adapt to Documenter v1 (#444)

* Adapt to Documenter v1
JuliaSparse · Oct 10, 2023 · e86b148 · e86b148
1 parent 034d234
commit e86b148
Show file tree

Hide file tree

Showing 4 changed files with 12 additions and 6 deletions.
diff --git a/docs/Project.toml b/docs/Project.toml
@@ -1,2 +1,5 @@
 [deps]
 Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
+
+[compat]
+Documenter = "1"
diff --git a/docs/make.jl b/docs/make.jl
@@ -10,8 +10,7 @@ makedocs(
         "SparseArrays" => "index.md",
         "Sparse Linear Algebra" => "solvers.md",
         ];
-    # strict = true,
-    strict = Symbol[:doctest],
+    warnonly = [:missing_docs, :cross_references],
     )
 
 deploydocs(repo = "github.com/JuliaSparse/SparseArrays.jl.git")
diff --git a/docs/src/solvers.md b/docs/src/solvers.md
@@ -14,7 +14,10 @@ Sparse matrix solvers call functions from [SuiteSparse](http://suitesparse.com).
 
 Other solvers such as [Pardiso.jl](https://github.com/JuliaSparse/Pardiso.jl/) are available as external packages. [Arpack.jl](https://julialinearalgebra.github.io/Arpack.jl/stable/) provides `eigs` and `svds` for iterative solution of eigensystems and singular value decompositions.
 
-These factorizations are described in more detail in [`Linear Algebra`](https://docs.julialang.org/en/v1/stdlib/LinearAlgebra/) section of the manual:
+These factorizations are described in more detail in the
+[`Linear Algebra`](https://docs.julialang.org/en/v1/stdlib/LinearAlgebra/)
+section of the manual:
+
 1. [`cholesky`](@ref SparseArrays.CHOLMOD.cholesky)
 2. [`ldlt`](@ref SparseArrays.CHOLMOD.ldlt)
 3. [`lu`](@ref SparseArrays.UMFPACK.lu)

diff --git a/src/solvers/cholmod.jl b/src/solvers/cholmod.jl
@@ -18,7 +18,8 @@ using LinearAlgebra
 using LinearAlgebra: RealHermSymComplexHerm, AdjOrTrans
 import LinearAlgebra: (\), AdjointFactorization,
                  cholesky, cholesky!, det, diag, ishermitian, isposdef,
-                 issuccess, issymmetric, ldlt, ldlt!, logdet, lowrankdowndate!
+                 issuccess, issymmetric, ldlt, ldlt!, logdet,
+                 lowrankdowndate, lowrankdowndate!, lowrankupdate, lowrankupdate!
 
 using SparseArrays
 using SparseArrays: getcolptr, AbstractSparseVecOrMat
@@ -1554,7 +1555,7 @@ factor will be `L*L' == P*A*P' + C'*C`
 
 `update`: `Cint(1)` for `A + CC'`, `Cint(0)` for `A - CC'`
 """
-lowrankdowndate!
+lowrankupdowndate!
 
 #Helper functions for rank updates
 lowrank_reorder(V::AbstractArray,p) = Sparse(sparse(V[p,:]))
@@ -1603,7 +1604,7 @@ lowrankupdate(F::Factor{Tv}, V::AbstractArray{Tv}) where {Tv<:VTypes} =
     lowrankupdate!(copy(F), V)
 
 """
-    lowrankupdate(F::CHOLMOD.Factor, C::AbstractArray) -> FF::CHOLMOD.Factor
+    lowrankdowndate(F::CHOLMOD.Factor, C::AbstractArray) -> FF::CHOLMOD.Factor
 
 Get an `LDLt` Factorization of `A + C*C'` given an `LDLt` or `LLt` factorization `F` of `A`.